LightNet: pruned sparsed convolution neural network for image classification
by Edna C. Too
International Journal of Computational Science and Engineering (IJCSE), Vol. 26, No. 3, 2023

Abstract: Deep learning has become the most sought-after approach in the area of artificial intelligence (AI). However, deep learning models pose some challenges in the learning process. It is computationally intensive to train deep learning networks and also resource-intensive. Therefore, it cannot be applicable in limited-resource devices. Limited research is being done on the implementation of efficient approaches for real-world problems. This study tries to bridge the gaps towards an applicable system in the real world especially in the agricultural sector for plants disease management and fruits classification. We introduce a novel deep learning architecture called LightNet. LightNet is an architecture that employs two strategies to achieve the sparsity of DenseNet: the skip connections and pruning strategy. The resultant is a small network with reduced parameters and model size. LightNet model matches the performance of the highest accuracy of DenseNet. Moreover, LightNet is × 2 smaller, × 2 parameters efficient and × 3 faster compared original DenseNet. The model is evaluated on the real-worlds dataset PlantsVillage and Fruits-360. The results show that our model achieved state-of-the-art results and it can be used for plant disease detection and fruits classification and grading.

Online publication date: Thu, 15-Jun-2023

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